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Record W4366771521 · doi:10.1002/jid.3784

Labour force participation of immigrant women in Canada: With a special focus on female immigrants from the Middle East and North Africa

2023· article· en· W4366771521 on OpenAlexafffundabout
Fariba Solati, Murshed Chowdhury, Fabiana Rosado

Bibliographic record

VenueJournal of International Development · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of New BrunswickSt. Thomas University
FundersCanadian Institutes of Health ResearchSocial Sciences and Humanities Research Council of CanadaSt. Thomas University
KeywordsImmigrationMiddle EastSocioeconomic statusDemographic economicsDemographyGeographyLogistic regressionSocioeconomicsPolitical scienceSociologyMedicineEconomicsPopulation

Abstract

fetched live from OpenAlex

Abstract Using the Longitudinal Survey of Immigrants to Canada (LSIC) and the linked LSIC‐Longitudinal Immigration Database (IMDB), this study finds that of all immigrant women in Canada, those from the Middle East and North Africa (MENA) region have the lowest labour force participation rate. After controlling for various socioeconomic factors and employing logistic regressions on multiple rounds of the LSIC and LSIC‐IMDB datasets, this study claims that patriarchal gender roles may have survived for MENA women even after a few years of living in Canada, resulting in relatively low labour force participation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0030.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.243
Teacher spread0.205 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2023
Admission routes3
Has abstractyes

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